Education

• Ph.D. in Statistics, University of Chicago, 2020 (expected)
• B.A. in Mathematics and Economics (Music Minor), Cornell University, 2013

Awards

• Davis Wallace Award for Applied Statistics, 2018
• Scholarship for Summer Institutes at the University of Washington in Seattle, 2017

Research

• De-mystifying Drop-outs in Single Cell UMI Data - Kim, T. Zhou, X. and Chen, M.
• Dynamic Gene Coexpression Analysis with Correlation Modeling - Kim, T. and Nicolae, D.
• Multivariate Bayesian Analysis with Incomplete Data: Application to Local Ancestry Effects on admixed Transcriptome - Kim, T. and Nicolae, D.
• Minimizing confounders and increasing data quality in murine models for studies of the gut microbiome - Miyoshi, J. et al.

Softwares

• HIPPO (Heterogeneity Inspired Pre-Processing Tool), available in BioConductor: HIPPO
• Multivariate Missing Bayesian Variable Selection, available in CRAN: MMVBVS
• Differential Network Analysis: diffNet
• Clustering Noisy Single Cell Data: SCNoisyClustering
• Change Point Analysis for Copy Number Variation: CopyNumberCellShift

Talks

• University of Chicago Consulting Seminar
  • Application of Neural Networks to Predicting Winter Wheat Yield
• University of Chicago, Medical School, Section of Genetic Medicine
  • Autoencoders with Parametric Noise Models for De-Noising scRNA-seq Data

Other Projects

• Noisy Data Clustering, advised by Mengjie Chen
  • design a similarity learning algorithm to cluster zero-inflated data with high noise and apply to single-cell sequencing data
• Copy Number Variation Change Point Analysis, advised by Mengjie Chen
  • devise an alternating descent algorithm combining group fused lasso and mixed effects model to detect copy number alterations in cancer cells
• Ancestry-eGenes, advised by Dan Nicolae
  • found genes that are differentially expressed based on local ancestry and genotypes for 44 human tissues in African Americans and European Americans; found an enrichment in the immunity-related region
• Microbiome Data Analysis, consulting team leader
  • Advised a manuscript revision in a medical journal for sound analysis of microbiome data, especially to model within and between group variance accounting for complex batch effects
• Effects of Maternal Language Use in Children’s Brain Development, consulting team leader
  • Led a consulting team to provide statistical analysis for complex correlated data for a psychologist’s post-doctoral project
• Computing Variance across Large Data Sets, advised by Lars Vilhuber
  • Manually implemented map-reduce for 3.3TB census data with several million time-series while avoiding memory issue and ensuring numerical stability

Work experience

• Zurich North America, 2019
  • Data Scientist Summer Intern
  • Built a neural network model to predict performance of winter wheat yields for the Multi-peril Crops Insurance that reduced RMSE by 10% compared to the existing GBM model
  • Conducted extensive research toe valuate the current state of the data for explaining the variability of crop yields
• Hanwha Life Insurance, 2013 - 2014
  • Actuarial Associate
  • participated as part of the product design team
  • revised contracts to abide by the amendments of national tax systems
• LnB Prep, 2014 - 2015
  • Instructor
  • taught SAT, ACT, and TOEFL
  • developed strategy textbooks for the new SAT

Skills

• Statistical Modeling and Inference
• Coding Languages
  • R, Rcpp, Python, Matlab, Java, bash, Julia (basic)
• Other Technical Skills
  • Git, TeX, Linux/Unix, HTML, MS Office
• Language
  • English, Korean

Courseworks

• Theoretical Statistics
  • Distribution Theory (STAT 304)
  • Mathematical Statistics 1 (STAT 301)
  • Mathematical Statistics 2: Bayesian Analysis and Principles (STAT 302)
  • Nonparametric Inference (STAT 374)
  • Multiple Testing, Modern Inference, and Replicability (STAT 308)
• Applied Statistics
  • Applied Linear Statistical Methods (STAT 343)
  • Design and Analysis of Experiments (STAT 345)
  • Generalized Linear Models (STAT 347)
  • Computational Biology: Models and Inference (STAT 354)
  • Statistical Genetics (STAT 355)
• Computational Statistics
  • Mathematical Computation I: Matrix Computation (STAT 309)
  • Mathematical Computation II: Nonlinear Optimization (STAT 310)
  • Machine Learning (STAT 377)
  • Machine Learning and Cancer (CMSC 337)
  • Machine Learning and Large-Scale Data Analysis (STAT 376)

Teaching

I served as a course assistant in Stat 331, Stat 226, and Stat 200 and was in charge of grading and answering the students’ questions. For Stat 234, I served as the lead TA who was in charge of the overall organization of the course.

• Stat 331 Sample Survey (Autumn 2016, Autumn 2017, Autumn 2018)
• Stat 234 Statistical Models and Methods I (Spring 2016, Spring 2017)
• Stat 226 Analysis of Categorical Data (Winter 2017, Winter 2019)
• Stat 200 Elementary Statistics (Winter 2015)

Other Activities

• Cornell University Chorus, 2011 - 2013
• Rockefeller Chapel Choir, 2015 - Present